Real-time streaming data ingestion into database tables

ABSTRACT

A streaming ingest platform can improve latency and expense issues related to uploading data into a cloud data system. The streaming ingest platform can organize the data to be ingested into per-table chunks and per-account blobs. This data may be committed and may be made available for query processing before it is ingested into the target source tables. This significantly improves latency issues. The streaming ingest platform can also accommodate uploading data from various sources with different processing and communication capabilities, such as Internet of Things (IOT) devices.

CROSS-REFERENCE TO PRIORITY APPLICATION

This application is a Continuation of U.S. patent application Ser. No.17/226,423, filed on Apr. 9, 2021, the content of which is herebyincorporated by reference in its entirety.

TECHNICAL FIELD

The present disclosure generally relates to a data systems, such asdatabases, and, more specifically to real-time streaming data ingestioninto a data system.

BACKGROUND

Data systems, such as database systems, may be provided through a cloudplatform, which allows organizations and users to store, manage, andretrieve data from the cloud. A variety of techniques can be employedfor uploading and storing data in a database or table in a cloudplatform.

To upload data into a data system, conventional systems typically use an“insert” or “copy” command. For example, a user can copy new data usinga “copy” command, which also necessitates the use of a running warehousefor transferring the data to the target table. This conventionalapproach suffers from significant drawbacks. These commands must bemanually initiated by a user. This manual initiation can cause latencyissues with respect to how fresh the data is in the target table,depending on how often the commands are initiated. This manualinitiation can also cause some or all the data to be lost if the taskfails. Also, operating a running warehouse to perform these commandstypically incurs large expenses. Some automated techniques suffer fromsimilar drawbacks of low latency and high expenses.

BRIEF DESCRIPTION OF THE DRAWINGS

Various ones of the appended drawings merely illustrate exampleembodiments of the present disclosure and should not be considered aslimiting its scope.

FIG. 1 illustrates an example computing environment in which anetwork-based data warehouse system can implement streams on shareddatabase objects, according to some example embodiments.

FIG. 2 is a block diagram illustrating components of a compute servicemanager, according to some example embodiments.

FIG. 3 is a block diagram illustrating components of an executionplatform, according to some example embodiments.

FIG. 4 shows a computing environment, according to some exampleembodiments.

FIG. 5 shows a flow diagram of a method for generating a blob, accordingto some example embodiments.

FIG. 6A shows a blob configuration, according to some exampleembodiments.

FIG. 6B shows a blob configuration, according to some exampleembodiments.

FIG. 7 shows a flow diagram of a method for committing a blob, accordingto some example embodiments.

FIG. 8 shows a computing environment, according to some exampleembodiments.

FIG. 9 shows a flow diagram of a method for generating and committing ablob, according to some example embodiments.

FIG. 10 shows a flow diagram of a method for processing a query,according to some example embodiments

FIG. 11 illustrates a diagrammatic representation of a machine in theform of a computer system within which a set of instructions may beexecuted for causing the machine to perform any one or more of themethodologies discussed herein, in accordance with some embodiments ofthe present disclosure.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

A streaming ingest platform, as described herein, can improve latencyand expense issues related to uploading data into a cloud data system.The streaming ingest platform can organize the data to be ingested intoper-table chunks in a per-account blob. This data may be committed andthen made available for query processing before it is ingested into thetarget source tables (e.g., converted to target source table format).This significantly improves latency issues. The streaming ingestplatform can also accommodate uploading data from various sources withdifferent processing and communication capabilities, such as Internet ofThings (IOT) devices. Moreover, the streaming ingest platform may uploadthe data asynchronously to provide more flexible control.

FIG. 1 illustrates an example shared data processing platform 100implementing secure messaging between deployments, in accordance withsome embodiments of the present disclosure. To avoid obscuring theinventive subject matter with unnecessary detail, various functionalcomponents that are not germane to conveying an understanding of theinventive subject matter have been omitted from the figures. However, askilled artisan will readily recognize that various additionalfunctional components may be included as part of the shared dataprocessing platform 100 to facilitate additional functionality that isnot specifically described herein.

As shown, the shared data processing platform 100 comprises thenetwork-based data warehouse system 102, a cloud computing storageplatform 104 (e.g., a storage platform, an AWS® service, MicrosoftAzure®, or Google Cloud Services®), and a remote computing device 106.The network-based data warehouse system 102 is a network-based systemused for storing and accessing data (e.g., internally storing data,accessing external remotely located data) in an integrated manner, andreporting and analysis of the integrated data from the one or moredisparate sources (e.g., the cloud computing storage platform 104). Thecloud computing storage platform 104 comprises a plurality of computingmachines and provides on-demand computer system resources such as datastorage and computing power to the network-based data warehouse system102. While in the embodiment illustrated in FIG. 1, a data warehouse isdepicted, other embodiments may include other types of databases orother data processing systems.

The remote computing device 106 (e.g., a user device such as a laptopcomputer) comprises one or more computing machines (e.g., a user devicesuch as a laptop computer) that execute a remote software component 108(e.g., browser accessed cloud service) to provide additionalfunctionality to users of the network-based data warehouse system 102.The remote software component 108 comprises a set of machine-readableinstructions (e.g., code) that, when executed by the remote computingdevice 106, cause the remote computing device 106 to provide certainfunctionality. The remote software component 108 may operate on inputdata and generates result data based on processing, analyzing, orotherwise transforming the input data. As an example, the remotesoftware component 108 can be a data provider or data consumer thatenables database tracking procedures, such as streams on shared tablesand views, as discussed in further detail below.

The network-based data warehouse system 102 comprises an accessmanagement system 110, a compute service manager 112, an executionplatform 114, and a database 116. The access management system 110enables administrative users to manage access to resources and servicesprovided by the network-based data warehouse system 102. Administrativeusers can create and manage users, roles, and groups, and usepermissions to allow or deny access to resources and services. Theaccess management system 110 can store shared data that securely managesshared access to the storage resources of the cloud computing storageplatform 104 amongst different users of the network-based data warehousesystem 102, as discussed in further detail below.

The compute service manager 112 coordinates and manages operations ofthe network-based data warehouse system 102. The compute service manager112 also performs query optimization and compilation as well as managingclusters of computing services that provide compute resources (e.g.,virtual warehouses, virtual machines, EC2 clusters). The compute servicemanager 112 can support any number of client accounts such as end usersproviding data storage and retrieval requests, system administratorsmanaging the systems and methods described herein, and othercomponents/devices that interact with compute service manager 112.

The compute service manager 112 is also coupled to database 116, whichis associated with the entirety of data stored on the shared dataprocessing platform 100. The database 116 stores data pertaining tovarious functions and aspects associated with the network-based datawarehouse system 102 and its users.

In some embodiments, database 116 includes a summary of data stored inremote data storage systems as well as data available from one or morelocal caches. Additionally, database 116 may include informationregarding how data is organized in the remote data storage systems andthe local caches. Database 116 allows systems and services to determinewhether a piece of data needs to be accessed without loading oraccessing the actual data from a storage device. The compute servicemanager 112 is further coupled to an execution platform 114, whichprovides multiple computing resources (e.g., virtual warehouses) thatexecute various data storage and data retrieval tasks, as discussed ingreater detail below.

Execution platform 114 is coupled to multiple data storage devices 124-1to 124-N that are part of a cloud computing storage platform 104. Insome embodiments, data storage devices 124-1 to 124-N are cloud-basedstorage devices located in one or more geographic locations. Forexample, data storage devices 124-1 to 124-N may be part of a publiccloud infrastructure or a private cloud infrastructure. Data storagedevices 124-1 to 124-N may be hard disk drives (HDDs), solid statedrives (SSDs), storage clusters, Amazon S3 storage systems or any otherdata storage technology. Additionally, cloud computing storage platform104 may include distributed file systems (such as Hadoop DistributedFile Systems (HDFS)), object storage systems, and the like.

The execution platform 114 comprises a plurality of compute nodes (e.g.,virtual warehouses). A set of processes on a compute node executes aquery plan compiled by the compute service manager 112. The set ofprocesses can include: a first process to execute the query plan; asecond process to monitor and delete micro-partition files using a leastrecently used (LRU) policy, and implement an out of memory (OOM) errormitigation process; a third process that extracts health informationfrom process logs and status information to send back to the computeservice manager 112; a fourth process to establish communication withthe compute service manager 112 after a system boot; and a fifth processto handle all communication with a compute cluster for a given jobprovided by the compute service manager 112 and to communicateinformation back to the compute service manager 112 and other computenodes of the execution platform 114.

The cloud computing storage platform 104 also comprises an accessmanagement system 118 and a web proxy 120. As with the access managementsystem 110, the access management system 118 allows users to create andmanage users, roles, and groups, and use permissions to allow or denyaccess to cloud services and resources. The access management system 110of the network-based data warehouse system 102 and the access managementsystem 118 of the cloud computing storage platform 104 can communicateand share information so as to enable access and management of resourcesand services shared by users of both the network-based data warehousesystem 102 and the cloud computing storage platform 104. The web proxy120 handles tasks involved in accepting and processing concurrent APIcalls, including traffic management, authorization and access control,monitoring, and API version management. The web proxy 120 provides HTTPproxy service for creating, publishing, maintaining, securing, andmonitoring APIs (e.g., REST APIs).

In some embodiments, communication links between elements of the shareddata processing platform 100 are implemented via one or more datacommunication networks. These data communication networks may utilizeany communication protocol and any type of communication medium. In someembodiments, the data communication networks are a combination of two ormore data communication networks (or sub-Networks) coupled to oneanother. In alternative embodiments, these communication links areimplemented using any type of communication medium and any communicationprotocol.

As shown in FIG. 1, data storage devices 124-1 to 124-N are decoupledfrom the computing resources associated with the execution platform 114.That is, new virtual warehouses can be created and terminated in theexecution platform 114 and additional data storage devices can becreated and terminated on the cloud computing storage platform 104 in anindependent manner. This architecture supports dynamic changes to thenetwork-based data warehouse system 102 based on the changing datastorage/retrieval needs as well as the changing needs of the users andsystems accessing the shared data processing platform 100. The supportof dynamic changes allows network-based data warehouse system 102 toscale quickly in response to changing demands on the systems andcomponents within network-based data warehouse system 102. Thedecoupling of the computing resources from the data storage devices124-1 to 124-N supports the storage of large amounts of data withoutrequiring a corresponding large amount of computing resources.Similarly, this decoupling of resources supports a significant increasein the computing resources utilized at a particular time withoutrequiring a corresponding increase in the available data storageresources. Additionally, the decoupling of resources enables differentaccounts to handle creating additional compute resources to process datashared by other users without affecting the other users' systems. Forinstance, a data provider may have three compute resources and sharedata with a data consumer, and the data consumer may generate newcompute resources to execute queries against the shared data, where thenew compute resources are managed by the data consumer and do not affector interact with the compute resources of the data provider.

Compute service manager 112, database 116, execution platform 114, cloudcomputing storage platform 104, and remote computing device 106 areshown in FIG. 1 as individual components. However, each of computeservice manager 112, database 116, execution platform 114, cloudcomputing storage platform 104, and remote computing environment may beimplemented as a distributed system (e.g., distributed across multiplesystems/platforms at multiple geographic locations) connected by APIsand access information (e.g., tokens, login data). Additionally, each ofcompute service manager 112, database 116, execution platform 114, andcloud computing storage platform 104 can be scaled up or down(independently of one another) depending on changes to the requestsreceived and the changing needs of shared data processing platform 100.Thus, in the described embodiments, the network-based data warehousesystem 102 is dynamic and supports regular changes to meet the currentdata processing needs.

During typical operation, the network-based data warehouse system 102processes multiple jobs (e.g., queries) determined by the computeservice manager 112. These jobs are scheduled and managed by the computeservice manager 112 to determine when and how to execute the job. Forexample, the compute service manager 112 may divide the job intomultiple discrete tasks and may determine what data is needed to executeeach of the multiple discrete tasks. The compute service manager 112 mayassign each of the multiple discrete tasks to one or more nodes of theexecution platform 114 to process the task. The compute service manager112 may determine what data is needed to process a task and furtherdetermine which nodes within the execution platform 114 are best suitedto process the task. Some nodes may have already cached the data neededto process the task (due to the nodes having recently downloaded thedata from the cloud computing storage platform 104 for a previous job)and, therefore, be a good candidate for processing the task. Metadatastored in the database 116 assists the compute service manager 112 indetermining which nodes in the execution platform 114 have alreadycached at least a portion of the data needed to process the task. One ormore nodes in the execution platform 114 process the task using datacached by the nodes and, if necessary, data retrieved from the cloudcomputing storage platform 104. It is desirable to retrieve as much dataas possible from caches within the execution platform 114 because theretrieval speed is typically much faster than retrieving data from thecloud computing storage platform 104.

As shown in FIG. 1, the shared data processing platform 100 separatesthe execution platform 114 from the cloud computing storage platform104. In this arrangement, the processing resources and cache resourcesin the execution platform 114 operate independently of the data storagedevices 124-1 to 124-N in the cloud computing storage platform 104.Thus, the computing resources and cache resources are not restricted tospecific data storage devices 124-1 to 124-N. Instead, all computingresources and all cache resources may retrieve data from, and store datato, any of the data storage resources in the cloud computing storageplatform 104.

FIG. 2 is a block diagram illustrating components of the compute servicemanager 112, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 2, a request processing service 202 managesreceived data storage requests and data retrieval requests (e.g., jobsto be performed on database data). For example, the request processingservice 202 may determine the data necessary to process a received query(e.g., a data storage request or data retrieval request). The data maybe stored in a cache within the execution platform 114 or in a datastorage device in cloud computing storage platform 104. A managementconsole service 204 supports access to various systems and processes byadministrators and other system managers. Additionally, the managementconsole service 204 may receive a request to execute a job and monitorthe workload on the system. The stream share engine 225 manages changetracking on database objects, such as a data share (e.g., shared table)or shared view, according to some example embodiments, and as discussedin further detail below.

The compute service manager 112 also includes a job compiler 206, a joboptimizer 208, and a job executor 210. The job compiler 206 parses a jobinto multiple discrete tasks and generates the execution code for eachof the multiple discrete tasks. The job optimizer 208 determines thebest method to execute the multiple discrete tasks based on the datathat needs to be processed. The job optimizer 208 also handles variousdata pruning operations and other data optimization techniques toimprove the speed and efficiency of executing the job. The job executor210 executes the execution code for jobs received from a queue ordetermined by the compute service manager 112.

A job scheduler and coordinator 212 sends received jobs to theappropriate services or systems for compilation, optimization, anddispatch to the execution platform 114. For example, jobs may beprioritized and processed in that prioritized order. In an embodiment,the job scheduler and coordinator 212 determines a priority for internaljobs that are scheduled by the compute service manager 112 with other“outside” jobs such as user queries that may be scheduled by othersystems in the database but may utilize the same processing resources inthe execution platform 114. In some embodiments, the job scheduler andcoordinator 212 identifies or assigns particular nodes in the executionplatform 114 to process particular tasks. A virtual warehouse manager214 manages the operation of multiple virtual warehouses implemented inthe execution platform 114. As discussed below, each virtual warehouseincludes multiple execution nodes that each include a cache and aprocessor (e.g., a virtual machine, an operating system level containerexecution environment).

Additionally, the compute service manager 112 includes a configurationand metadata manager 216, which manages the information related to thedata stored in the remote data storage devices and in the local caches(i.e., the caches in execution platform 114). The configuration andmetadata manager 216 uses the metadata to determine which datamicro-partitions need to be accessed to retrieve data for processing aparticular task or job. A monitor and workload analyzer 218 overseesprocesses performed by the compute service manager 112 and manages thedistribution of tasks (e.g., workload) across the virtual warehouses andexecution nodes in the execution platform 114. The monitor and workloadanalyzer 218 also redistributes tasks, as needed, based on changingworkloads throughout the network-based data warehouse system 102 and mayfurther redistribute tasks based on a user (e.g., “external”) queryworkload that may also be processed by the execution platform 114. Theconfiguration and metadata manager 216 and the monitor and workloadanalyzer 218 are coupled to a data storage device 220. Data storagedevice 220 in FIG. 2 represent any data storage device within thenetwork-based data warehouse system 102. For example, data storagedevice 220 may represent caches in execution platform 114, storagedevices in cloud computing storage platform 104, or any other storagedevice.

FIG. 3 is a block diagram illustrating components of the executionplatform 114, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 3, execution platform 114 includes multiplevirtual warehouses, which are elastic clusters of compute instances,such as virtual machines. In the example illustrated, the virtualwarehouses include virtual warehouse 1, virtual warehouse 2, and virtualwarehouse n. Each virtual warehouse (e.g., EC2 cluster) includesmultiple execution nodes (e.g., virtual machines) that each include adata cache and a processor. The virtual warehouses can execute multipletasks in parallel by using the multiple execution nodes. As discussedherein, execution platform 114 can add new virtual warehouses and dropexisting virtual warehouses in real time based on the current processingneeds of the systems and users. This flexibility allows the executionplatform 114 to quickly deploy large amounts of computing resources whenneeded without being forced to continue paying for those computingresources when they are no longer needed. All virtual warehouses canaccess data from any data storage device (e.g., any storage device incloud computing storage platform 104).

Although each virtual warehouse shown in FIG. 3 includes three executionnodes, a particular virtual warehouse may include any number ofexecution nodes. Further, the number of execution nodes in a virtualwarehouse is dynamic, such that new execution nodes are created whenadditional demand is present, and existing execution nodes are deletedwhen they are no longer necessary (e.g., upon a query or jobcompletion).

Each virtual warehouse is capable of accessing any of the data storagedevices 124-1 to 124-N shown in FIG. 1. Thus, the virtual warehouses arenot necessarily assigned to a specific data storage device 124-1 to124-N and, instead, can access data from any of the data storage devices124-1 to 124-N within the cloud computing storage platform 104.Similarly, each of the execution nodes shown in FIG. 3 can access datafrom any of the data storage devices 124-1 to 124-N. For instance, thestorage device 124-1 of a first user (e.g., provider account user) maybe shared with a worker node in a virtual warehouse of another user(e.g., consumer account user), such that the other user can create adatabase (e.g., read-only database) and use the data in storage device124-1 directly without needing to copy the data (e.g., copy it to a newdisk managed by the consumer account user). In some embodiments, aparticular virtual warehouse or a particular execution node may betemporarily assigned to a specific data storage device, but the virtualwarehouse or execution node may later access data from any other datastorage device.

In the example of FIG. 3, virtual warehouse 1 includes three executionnodes 302-1, 302-2, and 302-N. Execution node 302-1 includes a cache304-1 and a processor 306-1. Execution node 302-2 includes a cache 304-2and a processor 306-2. Execution node 302-N includes a cache 304-N and aprocessor 306-N. Each execution node 302-1, 302-2, and 302-N isassociated with processing one or more data storage and/or dataretrieval tasks. For example, a virtual warehouse may handle datastorage and data retrieval tasks associated with an internal service,such as a clustering service, a materialized view refresh service, afile compaction service, a storage procedure service, or a file upgradeservice. In other implementations, a particular virtual warehouse mayhandle data storage and data retrieval tasks associated with aparticular data storage system or a particular category of data.

Similar to virtual warehouse 1 discussed above, virtual warehouse 2includes three execution nodes 312-1, 312-2, and 312-N. Execution node312-1 includes a cache 314-1 and a processor 316-1. Execution node 312-2includes a cache 314-2 and a processor 316-2. Execution node 312-Nincludes a cache 314-N and a processor 316-N. Additionally, virtualwarehouse 3 includes three execution nodes 322-1, 322-2, and 322-N.Execution node 322-1 includes a cache 324-1 and a processor 326-1.Execution node 322-2 includes a cache 324-2 and a processor 326-2.Execution node 322-N includes a cache 324-N and a processor 326-N.

In some embodiments, the execution nodes shown in FIG. 3 are statelesswith respect to the data the execution nodes are caching. For example,these execution nodes do not store or otherwise maintain stateinformation about the execution node, or the data being cached by aparticular execution node. Thus, in the event of an execution nodefailure, the failed node can be transparently replaced by another node.Since there is no state information associated with the failed executionnode, the new (replacement) execution node can easily replace the failednode without concern for recreating a particular state.

Although the execution nodes shown in FIG. 3 each include one data cacheand one processor, alternative embodiments may include execution nodescontaining any number of processors and any number of caches.Additionally, the caches may vary in size among the different executionnodes. The caches shown in FIG. 3 store, in the local execution node(e.g., local disk), data that was retrieved from one or more datastorage devices in cloud computing storage platform 104 (e.g., S3objects recently accessed by the given node). In some exampleembodiments, the cache stores file headers and individual columns offiles as a query downloads only columns necessary for that query.

To improve cache hits and avoid overlapping redundant data stored in thenode caches, the job optimizer 208 assigns input file sets to the nodesusing a consistent hashing scheme to hash over table file names of thedata accessed (e.g., data in database 116 or database 122). Subsequentor concurrent queries accessing the same table file will therefore beperformed on the same node, according to some example embodiments.

As discussed, the nodes and virtual warehouses may change dynamically inresponse to environmental conditions (e.g., disaster scenarios),hardware/software issues (e.g., malfunctions), or administrative changes(e.g., changing from a large cluster to smaller cluster to lower costs).In some example embodiments, when the set of nodes changes, no data isreshuffled immediately. Instead, the least recently used replacementpolicy is implemented to eventually replace the lost cache contents overmultiple jobs. Thus, the caches reduce or eliminate the bottleneckproblems occurring in platforms that consistently retrieve data fromremote storage systems. Instead of repeatedly accessing data from theremote storage devices, the systems and methods described herein accessdata from the caches in the execution nodes, which is significantlyfaster and avoids the bottleneck problem discussed above. In someembodiments, the caches are implemented using high-speed memory devicesthat provide fast access to the cached data. Each cache can store datafrom any of the storage devices in the cloud computing storage platform104.

Further, the cache resources and computing resources may vary betweendifferent execution nodes. For example, one execution node may containsignificant computing resources and minimal cache resources, making theexecution node useful for tasks that require significant computingresources. Another execution node may contain significant cacheresources and minimal computing resources, making this execution nodeuseful for tasks that require caching of large amounts of data. Yetanother execution node may contain cache resources providing fasterinput-output operations, useful for tasks that require fast scanning oflarge amounts of data. In some embodiments, the execution platform 114implements skew handling to distribute work amongst the cache resourcesand computing resources associated with a particular execution, wherethe distribution may be further based on the expected tasks to beperformed by the execution nodes. For example, an execution node may beassigned more processing resources if the tasks performed by theexecution node become more processor-intensive. Similarly, an executionnode may be assigned more cache resources if the tasks performed by theexecution node require a larger cache capacity. Further, some nodes maybe executing much slower than others due to various issues (e.g.,virtualization issues, network overhead). In some example embodiments,the imbalances are addressed at the scan level using a file stealingscheme. In particular, whenever a node process completes scanning itsset of input files, it requests additional files from other nodes. Ifthe one of the other nodes receives such a request, the node analyzesits own set (e.g., how many files are left in the input file set whenthe request is received), and then transfers ownership of one or more ofthe remaining files for the duration of the current job (e.g., query).The requesting node (e.g., the file stealing node) then receives thedata (e.g., header data) and downloads the files from the cloudcomputing storage platform 104 (e.g., from data storage device 124-1),and does not download the files from the transferring node. In this way,lagging nodes can transfer files via file stealing in a way that doesnot worsen the load on the lagging nodes.

Although virtual warehouses 1, 2, and n are associated with the sameexecution platform 114, the virtual warehouses may be implemented usingmultiple computing systems at multiple geographic locations. Forexample, virtual warehouse 1 can be implemented by a computing system ata first geographic location, while virtual warehouses 2 and n areimplemented by another computing system at a second geographic location.In some embodiments, these different computing systems are cloud-basedcomputing systems maintained by one or more different entities.

Additionally, each virtual warehouse is shown in FIG. 3 as havingmultiple execution nodes. The multiple execution nodes associated witheach virtual warehouse may be implemented using multiple computingsystems at multiple geographic locations. For example, an instance ofvirtual warehouse 1 implements execution nodes 302-1 and 302-2 on onecomputing platform at a geographic location and implements executionnode 302-N at a different computing platform at another geographiclocation. Selecting particular computing systems to implement anexecution node may depend on various factors, such as the level ofresources needed for a particular execution node (e.g., processingresource requirements and cache requirements), the resources availableat particular computing systems, communication capabilities of networkswithin a geographic location or between geographic locations, and whichcomputing systems are already implementing other execution nodes in thevirtual warehouse.

Execution platform 114 is also fault tolerant. For example, if onevirtual warehouse fails, that virtual warehouse is quickly replaced witha different virtual warehouse at a different geographic location.

A particular execution platform 114 may include any number of virtualwarehouses. Additionally, the number of virtual warehouses in aparticular execution platform is dynamic, such that new virtualwarehouses are created when additional processing and/or cachingresources are needed. Similarly, existing virtual warehouses may bedeleted when the resources associated with the virtual warehouse are nolonger necessary.

In some embodiments, the virtual warehouses may operate on the same datain cloud computing storage platform 104, but each virtual warehouse hasits own execution nodes with independent processing and cachingresources. This configuration allows requests on different virtualwarehouses to be processed independently and with no interferencebetween the requests. This independent processing, combined with theability to dynamically add and remove virtual warehouses, supports theaddition of new processing capacity for new users without impacting theperformance observed by the existing users.

Next, techniques for real-time streaming data ingestion into a datasystem will be described. FIG. 4 shows an example of a computingenvironment, according to some example embodiments. The computingenvironment may include a client 402, a storage 404, a query globalservice (GS) 406, a commit service 408, and a database 410.

The client 402 may include a software development kit (SDK) to runsoftware programs to generate the file structures described herein. Theclient 402 may communicate with the data system to upload data to beingested into one or more tables. The client 402 may open one or morechannels to the data system. A channel may be a logical connection to aparticular table stored in the data system, such as in the database 410.The data system, e.g., in the database 410, may include a plurality oftables associated with an account for the client 402.

The client 402 may write new data, such as rows, into the one or morechannels. The client 402 may include a buffer. The client 402 may bufferthe outgoing data into per-table sets (also referred to as “chunks”herein). A chunk may be associated with a single table (i.e., all datain a chunk is addressed to a single table); however, a table may beassociated with a plurality of chunks at a time. The data in the chunksmay be provided in a first format. For example, the data in the chunksmay be provided in the format used by the client 402 (e.g., Arrowformat). The chunks may be buffered into per-account groups (alsoreferred to as “blobs” herein). A blob may contain chunks associatedwith different tables of the same account.

The buffer may include a threshold, e.g., a size or time threshold. Whenthe threshold is crossed or exceeded, the client 402 may transfer theblobs in its buffer to the storage 404. The storage 404 may be providedas cloud storage and may be a part of the data system. Additionally oralternatively, the storage 404 may be provided as internal storage ofthe data system. In another embodiment, the storage 404 may be providedas external storage to the data system. In any event, the data systemmay have access to the storage 404. The client 402 may write the blobsto an internal stage in the storage 404. The client 402 may use an APIfor the storage 404 to perform the write operation. The storage 404 mayreceive the blobs from the client 402 and may store them in the firstformat.

The client 402 may also transmit a registration request to the query GS406 in the data system. The registration request may include informationabout the blobs stored at the storage 404, such as identificationinformation for the blobs and the location where the blobs are stored(e.g., network address). The client 402 may register the blobs via aREST API call to the query GS 406.

The query GS 406 may communicate with the commit service 408. The queryGS 406 may fan the blob registration requests into per-table chunkregistration requests and transmit the per-table chunk registrationrequests to the commit service 408. The commit service 408 may queue theper-table chunks and may validate and dedupe them using sequencinginformation, described in further detail below. The commit service 408may fast commit the data via RPC to generate a hybrid table. The commitservice 408 may write the data to a metadata store to commit the data.The committing of the data may be used to generate a hybrid table, whichcan be used for immediate query processing of the committed data. Thehybrid table may include data from the blobs in a first format (e.g.,Arrow format) and data in the one or more tables stored in database 410in a second format (e.g., FDN). The hybrid table may make the data inthe blobs in the storage 404 available for query processing, asdescribed in further detail below.

The database 410 may store the tables associated with differentaccounts. Each account may have one or more tables associated therewith.The data stored in the blobs (organized by per-table chunks) in thestorage 404 may be ingested into corresponding tables in the database410 after it has been committed. After the data is ingested into thecorresponding tables in the database 410, that data may be removed fromthe storage 404 (e.g., flushed).

FIG. 5 shows a flow diagram of a method 500 for generating a blob,according to some example embodiments. In an example, portions of themethod 500 may be performed by the client 402 (e.g., using a clientSDK). At operation 502, the client may open one or more channels to atable stored in the data system. The number of channels may depend onthe amount of data to be transferred to the data system. The number ofchannels may depend on the number of tables implicated in the datatransfer. At operation 504, the client may write data (e.g., rows) intothe channels. The data may be in a first format (e.g., Arrow format). Atoperation 506, the data may be buffered into per-table chunks in thefirst format. Hence, each chunk may contain data for only one table. Atoperation 508, the chunks may be buffered into per-account blobs. Hence,each blob may contain data for one or more tables but for only oneaccount. An account may have a plurality of tables associated with it inthe data system.

The client may also insert ordering or sequencing information. Forexample, the client may insert client and row sequencing information.The client sequencing information may be related to the channel. Eachchannel may be client specific; hence, only one client may haveownership of a channel at a time. The client sequencing information mayprevent concurrent usage of the channel by multiple clients (accounts).The data system may use the client sequencing information to identifythe present owner of the channel and prevent other accounts from usingthe same channel.

The row sequencing information may provide information related to eachrecord or row. Each record or row may be stamped with a row sequencer.This row sequencing information may then be used by the data system tocheck for duplicate data or gaps in the data. For example, if the datasystem receives two records with the same row sequencing information, itmay detect a duplicate. On the other hand, if the data system receivesrecord 1 and then record 3, it may detect that it did not receive record2. Also, the row sequencing information may be used to maintain theordering of the data.

The client may also insert an offset token. The client may use theoffset token information in case there was an error in the blobtransmission. In the case of a client failure, the offset token mayindicate which data was correctly received by the data system, so theclient may restart its transmission without having to duplicate alreadyreceived data and without including gaps in the data.

The buffer may have a threshold associated therewith. For example, thebuffer may include a size and/or time threshold. At operation 510, theclient may write the blob into storage. For example, the client maywrite the blob into storage in response to the buffer's threshold beingexceeded. The client may write the blob to an internal stage associatedwith the account in the storage (e.g., Streaming Ingest internal stage).The client may use storage API to perform the write operation.

At operation 512, the client may register the blob with the query GS inthe data system. The client may transmit a registration request to thequery GS. The registration request may include information about theblob, such as identification information for the blob and the locationwhere the blob is stored, such as an address. The client may registerthe blob via a REST API call to the query GS. After the data systemregisters the blob and commits the data therein, it may transmit aconfirmation to the client. The client may receive the confirmation.Once the data is committed and before it is ingested into the sourcetable, that data may be available for query processing. From the clientperspective, the committed data and data in the tables may be availablethe same way for query processing.

FIG. 6A shows an example of a blob 600, according to some exampleembodiments. The blob 600 may include a header 602 and one or morechunks 604, 606, 608, 610. The header 602 may include informationrelating blob version, client sequencing (e.g., client sequence number),row sequencing (e.g., row sequence number), tables contained in blob,offset token, and byte ranges of the chunks. The header may also includeexpression property (EP) information about the chunks. The EPinformation may include statistics for the chunks 604-610. For example,the EP information may include min-max of a chunk, number of rows, andother statistics. As discussed in further detail below, the data systemmay use this EP information for optimizing and pruning query processingfor the committed data.

As mentioned above, the chunks may be defined per-table. For example,chunk 604 may be associated with table A; chunks 606 and 608 may beassociated with table B; and chunk 610 may be associated with table C.The data in the blob 600 may be in the native (first) format of theclient (e.g., Arrow format).

FIG. 6B shows another example of a blob 650, according to some exampleembodiments. The blob 650 may include a blob name field 652, a versionfield 654, a file size field 656, a checksum field 658, chunks metadatalength field 660, and chunks metadata field 662. The blob 650 may alsothen include chunk data and EP information about each chunk. Forexample, blob 650 may include Chunk 1 EP Data 664 and Chunk 1 Data 666,Chunk 2 EP Data 668 and Chunk 2 Data 670, and Chunk 3 EP Data 672 andChunk 3 EP Data 674. The data in the chunks may be in the native (first)format of the client (e.g., Arrow format).

FIG. 7 shows a flow diagram of a method 700 for committing a blob,according to some example embodiments. At operation 702, the data system(e.g., query GS) may receive a registration request for the blob. Theregistration request may include information about the blob, such asidentification information for the blob and the location where the blobis stored, such as an address.

At operation 704, the data system may access the stored blob using theinformation provided in the registration request. The data in the blob(e.g., chunks) may be stored in their native format, which is referredto as a first format herein. At operation 706, the query GS may dividethe blob registration request into per-table chunk registration requestsand transmit the per-table chunk registration requests to the commitservice. At operation 708, the commit service may queue the per-tablechunks and may validate and dedupe the data using sequencinginformation. For example, the commit service may use the client and rowsequencing information to validate the incoming data. If an error isdetected, the data system may send a notification to the client. Forexample, the data system may transmit the most recent offset tokeninformation to the client, instructing the client to transmit the databased on the offset token information.

At operation 710, the commit service may commit the data via RPC togenerate a hybrid table. Data may be written to a metadata store tocommit the incoming data. The hybrid table may include data from thechunks in a first format (e.g., Arrow format) and data in the one ormore tables stored in database associated with the common account in asecond format (e.g., FDN).

At operation 712, the commit service may make the data in the blobavailable for query processing, as described in further detail below.The hybrid table may allow query processing of data in the blob which isnot yet ingested into source tables.

At operation 714, the committed data may be ingested or migrated intotheir corresponding source table. The ingested data may create newpartitions in the source table. The ingestion may be performed using avariety of techniques. In one embodiment, DML operation on the committeddata may initiate ingestion. For example, if a DML operation touchesupon a section (e.g., a row) of the committed data, that section or thechunk associated with that section may be ingested or migrated to thesource table in response to the DML operation. Moreover, a backgroundservice may also operate to ingest the committed data at specified times(e.g., intervals). After the committed data has been ingested, that datamay be removed from storage.

In the embodiments described above, blob generation was handledprimarily by the client (e.g., using client SDK). However, some or allblob generation responsibilities may be performed by the data system.FIG. 8 shows an example of a computing environment, according to someexample embodiments. The computing environment may include a client 802,a storage 804, a query global service (GS) 806, a commit service 808, adatabase 810, and a buffer service 812.

Here, the client 802 may be less robust as the client 402 describedabove with reference to FIG. 4. The client 802 may communicate with thedata system via HTTP calls using REST API, a thin client SDK wrapper, orthe like. For example, the client 802 may be provided as Intent ofThings (JOT) device, such as an appliance, lights, etc., which may havelimited processing and communication capabilities. The client 802 maycommunicate with the data system to upload data to be ingested into oneor more tables. The client 802 may open one or more channels to the datasystem. A channel may be a logical connection to a particular tablestored in the data system. The data system may include a plurality oftables associated with an account for the client 802.

The client 802 may write new data, such as rows, into the one or morechannels. The client 802 may transmit the data to the query GS 806. Theclient 802 may also insert sequencing information (e.g., channel and rowsequence numbers) and/or offset token information, as described above.

The query service 806 may forward the data received from the client 802to the buffer service 812. The buffer service 812 may generate chunksand blobs from the received data as described herein. The buffer service812 may validate, dedupe, and aggregate data (e.g., rows) from one ormore channels associated with the account for the client 802 intoper-table sets (also referred to as “chunks” herein). The data in thechunks may be provided in a first format. For example, the data in thechunks may be provided in the format used by the client 802 (e.g., Arrowformat). The chunks may be arranged into per-account groups (alsoreferred to as “blobs” herein).

The buffer service 812 may include a threshold, e.g., a size or timethreshold. When the threshold is crossed or exceeded, the buffer service812 may write the blobs to the storage 804. The storage 804 may beprovided as cloud storage and may be a part of the data system.Additionally or alternatively, the storage 804 may be provided asinternal storage of the data system. In another embodiment, the storage804 may be provided as external storage to the data system. In anyevent, the data system may have access to the storage 804. The storage804 may receive the blobs from the client 802 and may store them in thefirst format. The buffer service 812 may write the blobs to an internalstage in the storage 804. The buffer service 812 may use an API for thestorage 804 to perform the write operation.

The buffer service 812 may communicate with the commit service 808. Thebuffer service 812 may fan out the per-table chunk registration requestsand transmit the per-table chunk registration requests to the commitservice 808. The buffer service 812 may provide information for theblobs and the location where the blobs are stored, such as an address.The commit service 808 may queue the per-table chunks and may validateand dedupe then using sequencing information. The commit service 808 mayfast commit the data via RPC to generate a hybrid table. The queryservice 808 may write the data to a metadata store to commit the data.The hybrid table may include data from the blobs in a first format(e.g., Arrow format) and data in the one or more tables stored indatabase 810 in a second format (e.g., FDN). The hybrid table may makethe data in the blobs in the storage 804 available for query processingbefore the data is ingested into the one or more tables stored in thedatabase 810, as described in further detail below.

The database 810 may store the tables associated with differentaccounts. Each account may have one or more tables associated therewith.The data stored in the blobs (organized by per-table chunks) in thestorage 804 may be ingested into corresponding tables in the database810. After the data is ingested into the corresponding tables in thedatabase 810, that data may be removed from the storage 804.

FIG. 9 shows a flow diagram of a method 900 for generating andcommitting a blob, according to some example embodiments. The method 900may be executed by a data system and its components. At 902, the datasystem may receive the incoming data from a client via one or morechannels, for example, as described above with reference to FIG. 8. Theincoming data may be received via a HTTP call. The incoming data mayinclude client and row sequencing information and offset tokeninformation. The incoming data may also include related EP information.

At operation 904, the data may be buffered into per-table chunks in thefirst format. Hence, each chunk may contain data for only one table. Atoperation 906, the chunks may be buffered into per-account blobs. Hence,each blob may contain data for one or more tables but for only oneaccount. An account may have a plurality of tables associated with it inthe data system. The data system may include a buffer having a thresholdassociated therewith. For example, the buffer may include a size and/ortime threshold.

At operation 908, the data system (e.g., buffer service) may write theblob into storage. For example, the buffer service may write the blobinto storage in response to the buffer's threshold being exceeded. Thebuffer service may write the blob to an internal stage associated withthe account in the storage (e.g., Streaming Ingest internal stage). Thebuffer service may use storage API to perform the write operation. Thedata in the blob (e.g., chunks) may be stored in their native format,which is referred to as a first format herein.

At 910, the data system (e.g., buffer service or query GS) may generateper-table chunk registration requests and transmit the per-table chunkregistration requests to the commit service. At 912, the commit servicemay queue the per-table chunks and may validate and dedupe the datausing sequencing information. For example, the commit service may usethe client and row sequencing information to validate the incoming data.If an error is detected, the data system may send a notification to theclient. For example, the data system may transmit the most recent offsettoken information to the client, instructing the client to transmit thedata based on the offset token information.

At operation 914, the commit service may commit the data via RPC togenerate a hybrid table. Data may be written to a metadata store tocommit the incoming data. The hybrid table may include data from thechunks in a first format (e.g., Arrow format) and data in the one ormore tables stored in a database in a second format (e.g., FDN).

At operation 916, the commit service may make the data in the blobavailable for query processing, as described in further detail below.The hybrid table may allow query processing of data in the blob which isnot yet ingested into source tables.

At operation 918, the committed data may be ingested or migrated intotheir corresponding source table. The ingested data may create newpartitions in the source table. The ingestion may be performed using avariety of techniques. In one embodiment, DML operation on the committeddata may initiate ingestion. For example, if a DML operation touchesupon a section (e.g., a row) of the committed data, that section or thechunk associated with that section may be ingested or migrated to thesource table in response to the DML operation. Moreover, a backgroundservice may also operate to ingest the committed data at specified times(e.g., intervals). After committed data has been ingested, that data maybe removed from storage.

FIG. 10 shows a flow diagram of a method 1000 for processing a query,according to some example embodiments. At operation 1002, the datasystem may receive a query. The query may relate to data stored in asource table stored in the data system and data committed for that tablebut not yet ingested into the source table, as described herein. Hence,the data system may process the query using both the data stored in thesource table and the committed data stored in storage as a hybrid table,as described herein. The data may be provided into two formats, asdescribed herein. The committed data may be stored in a first format(e.g., Arrow) and the data in the source table may be stored in a secondformat (e.g., FDN).

At operation 1004, the data for the query may be partitioned intodifferent scansets, e.g., one for the data in the source table and onefor the committed data. At operation 1006, the scansets may be prunedbased on expression properties of the scansets. For example, the EPinformation associated with the chunks in the blobs of the committeddata may be used to prune the committed data scanset. At operation 1008,the different scansets may be scanned and may be converted to a commonformat. The different format information may be converted to a commonin-memory format during the scanning. At operation 1010, the differentscansets, now converted to the common in-memory format, may be joined(e.g., union operator) for query execution. This joining of thecommitted data and source table data may present a unified view of thedata by way of the hybrid table. At operation 1012, the query may beexecuted using the joined data, and a result of the query may begenerated and transmitted to the requester of the query.

The techniques described herein provide benefits over other ingestiontechniques. The techniques described herein provide direct datastreaming to source tables over http calls without using other complexcomponents. Hence, the techniques provide lower overheard with minimalconfiguration while providing high throughput. The techniques maintainordering information of the new data. The ordering information from theclient is maintained, and no other ordering may not be performed by thedata system. Moreover, the techniques provide low latency and low cost.The new data is available for query after it is committed and before itis ingested as described herein (e.g., the use of the hybrid table).Thus, the data may be available for querying almost immediately (e.g., afew seconds).

FIG. 11 illustrates a diagrammatic representation of a machine 1100 inthe form of a computer system within which a set of instructions may beexecuted for causing the machine 1100 to perform any one or more of themethodologies discussed herein, according to an example embodiment.Specifically, FIG. 11 shows a diagrammatic representation of the machine1100 in the example form of a computer system, within which instructions1116 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1100 to perform any oneor more of the methodologies discussed herein may be executed. Forexample, the instructions 1116 may cause the machine 1100 to execute anyone or more operations of any one or more of the methods describedherein. As another example, the instructions 1116 may cause the machine1100 to implement portions of the data flows described herein. In thisway, the instructions 1116 transform a general, non-programmed machineinto a particular machine 1100 (e.g., the remote computing device 106,the access management system 110, the compute service manager 112, theexecution platform 114, the access management system 118, the Web proxy120, remote computing device 106) that is specially configured to carryout any one of the described and illustrated functions in the mannerdescribed herein.

In alternative embodiments, the machine 1100 operates as a standalonedevice or may be coupled (e.g., networked) to other machines. In anetworked deployment, the machine 1100 may operate in the capacity of aserver machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1100 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a smart phone, a mobiledevice, a network router, a network switch, a network bridge, or anymachine capable of executing the instructions 1116, sequentially orotherwise, that specify actions to be taken by the machine 1100.Further, while only a single machine 1100 is illustrated, the term“machine” shall also be taken to include a collection of machines 1100that individually or jointly execute the instructions 1116 to performany one or more of the methodologies discussed herein.

The machine 1100 includes processors 1110, memory 1130, and input/output(I/O) components 1150 configured to communicate with each other such asvia a bus 1102. In an example embodiment, the processors 1110 (e.g., acentral processing unit (CPU), a reduced instruction set computing(RISC) processor, a complex instruction set computing (CISC) processor,a graphics processing unit (GPU), a digital signal processor (DSP), anapplication-specific integrated circuit (ASIC), a radio-frequencyintegrated circuit (RFIC), another processor, or any suitablecombination thereof) may include, for example, a processor 1112 and aprocessor 1114 that may execute the instructions 1116. The term“processor” is intended to include multi-core processors 1110 that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions 1116 contemporaneously. AlthoughFIG. 11 shows multiple processors 1110, the machine 1100 may include asingle processor with a single core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiple cores, or any combinationthereof.

The memory 1130 may include a main memory 1132, a static memory 1134,and a storage unit 1136, all accessible to the processors 1110 such asvia the bus 1102. The main memory 1132, the static memory 1134, and thestorage unit 1136 store the instructions 1116 embodying any one or moreof the methodologies or functions described herein. The instructions1116 may also reside, completely or partially, within the main memory1132, within the static memory 1134, within the storage unit 1136,within at least one of the processors 1110 (e.g., within the processor'scache memory), or any suitable combination thereof, during executionthereof by the machine 1100.

The I/O components 1150 include components to receive input, provideoutput, produce output, transmit information, exchange information,capture measurements, and so on. The specific I/O components 1150 thatare included in a particular machine 1100 will depend on the type ofmachine. For example, portable machines such as mobile phones willlikely include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 1150 mayinclude many other components that are not shown in FIG. 11. The I/Ocomponents 1150 are grouped according to functionality merely forsimplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 1150 mayinclude output components 1152 and input components 1154. The outputcomponents 1152 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), other signal generators, and soforth. The input components 1154 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1150 may include communication components 1164operable to couple the machine 1100 to a network 1180 or devices 1170via a coupling 1182 and a coupling 1172, respectively. For example, thecommunication components 1164 may include a network interface componentor another suitable device to interface with the network 1180. Infurther examples, the communication components 1164 may include wiredcommunication components, wireless communication components, cellularcommunication components, and other communication components to providecommunication via other modalities. The devices 1170 may be anothermachine or any of a wide variety of peripheral devices (e.g., aperipheral device coupled via a universal serial bus (USB)). Forexample, as noted above, the machine 1100 may correspond to any one ofthe remote computing device 106, the access management system 110, thecompute service manager 112, the execution platform 114, the accessmanagement system 118, the Web proxy 120, and the devices 1170 mayinclude any other of these systems and devices.

The various memories (e.g., 1130, 1132, 1134, and/or memory of theprocessor(s) 1110 and/or the storage unit 1136) may store one or moresets of instructions 1116 and data structures (e.g., software) embodyingor utilized by any one or more of the methodologies or functionsdescribed herein. These instructions 1116, when executed by theprocessor(s) 1110, cause various operations to implement the disclosedembodiments.

As used herein, the terms “machine-storage medium,” “device-storagemedium,” and “computer-storage medium” mean the same thing and may beused interchangeably in this disclosure. The terms refer to a single ormultiple storage devices and/or media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storeexecutable instructions and/or data. The terms shall accordingly betaken to include, but not be limited to, solid-state memories, andoptical and magnetic media, including memory internal or external toprocessors. Specific examples of machine-storage media, computer-storagemedia, and/or device-storage media include non-volatile memory,including by way of example semiconductor memory devices, e.g., erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), field-programmable gate arrays(FPGAs), and flash memory devices; magnetic disks such as internal harddisks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The terms “machine-storage media,” “computer-storage media,” and“device-storage media” specifically exclude carrier waves, modulateddata signals, and other such media, at least some of which are coveredunder the term “signal medium” discussed below.

In various example embodiments, one or more portions of the network 1180may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local-area network (LAN), a wireless LAN (WLAN), awide-area network (WAN), a wireless WAN (WWAN), a metropolitan-areanetwork (MAN), the Internet, a portion of the Internet, a portion of thepublic switched telephone network (PSTN), a plain old telephone service(POTS) network, a cellular telephone network, a wireless network, aWi-Fi® network, another type of network, or a combination of two or moresuch networks. For example, the network 1180 or a portion of the network1180 may include a wireless or cellular network, and the coupling 1182may be a Code Division Multiple Access (CDMA) connection, a GlobalSystem for Mobile communications (GSM) connection, or another type ofcellular or wireless coupling. In this example, the coupling 1182 mayimplement any of a variety of types of data transfer technology, such asSingle Carrier Radio Transmission Technology (1×RTT), Evolution-DataOptimized (EVDO) technology, General Packet Radio Service (GPRS)technology, Enhanced Data rates for GSM Evolution (EDGE) technology,third Generation Partnership Project (3GPP) including 3G, fourthgeneration wireless (4G) networks, Universal Mobile TelecommunicationsSystem (UMTS), High-Speed Packet Access (HSPA), WorldwideInteroperability for Microwave Access (WiMAX), Long Term Evolution (LTE)standard, others defined by various standard-setting organizations,other long-range protocols, or other data transfer technology.

The instructions 1116 may be transmitted or received over the network1180 using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components1164) and utilizing any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions1116 may be transmitted or received using a transmission medium via thecoupling 1172 (e.g., a peer-to-peer coupling) to the devices 1170. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure. The terms “transmissionmedium” and “signal medium” shall be taken to include any intangiblemedium that is capable of storing, encoding, or carrying theinstructions 1116 for execution by the machine 1100, and include digitalor analog communications signals or other intangible media to facilitatecommunication of such software. Hence, the terms “transmission medium”and “signal medium” shall be taken to include any form of modulated datasignal, carrier wave, and so forth. The term “modulated data signal”means a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in the signal.

The terms “machine-readable medium,” “computer-readable medium,” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms are defined to includeboth machine-storage media and transmission media. Thus, the termsinclude both storage devices/media and carrier waves/modulated datasignals.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Similarly, the methods described hereinmay be at least partially processor-implemented. For example, at leastsome of the operations of the methods described herein may be performedby one or more processors. The performance of certain of the operationsmay be distributed among the one or more processors, not only residingwithin a single machine, but also deployed across a number of machines.In some example embodiments, the processor or processors may be locatedin a single location (e.g., within a home environment, an officeenvironment, or a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

Although the embodiments of the present disclosure have been describedwith reference to specific example embodiments, it will be evident thatvarious modifications and changes may be made to these embodimentswithout departing from the broader scope of the inventive subjectmatter. Accordingly, the specification and drawings are to be regardedin an illustrative rather than a restrictive sense. The accompanyingdrawings that form a part hereof show, by way of illustration, and notof limitation, specific embodiments in which the subject matter may bepracticed. The embodiments illustrated are described in sufficientdetail to enable those skilled in the art to practice the teachingsdisclosed herein. Other embodiments may be used and derived therefrom,such that structural and logical substitutions and changes may be madewithout departing from the scope of this disclosure. This DetailedDescription, therefore, is not to be taken in a limiting sense, and thescope of various embodiments is defined only by the appended claims,along with the full range of equivalents to which such claims areentitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent, to those of skill inthe art, upon reviewing the above description.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended; that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim is still deemed to fall within thescope of that claim.

The following numbered examples are embodiments:

Example 1. A method comprising: receiving data from a client via one ormore channels for ingestion into one or more source tables in a datasystem; storing the received data in a storage in a first format; basedon a registration request for the received data, committing, by aprocessor, the received data stored in the storage and making thereceived data in the first format accessible for query processing beforethe received data is ingested into the one or more source tables; andingesting the received data into the one or more source tables in asecond format.

Example 2. The method of example 2, wherein the received data isorganized into per-table sets, data in each set belonging to a singlesource table.

Example 3. The method of any of examples 1-2, wherein per-table sets areorganized into per-account groups, data in each group belonging to asingle account.

Example 4. The method of any of examples 1-3, further comprising:writing the received data to a metadata store; and generating a hybridtable for query processing, the hybrid table including the committeddata in the first format and data from the one or more source tables inthe second table.

Example 5. The method of any of examples 1-4, further comprising: forquery processing: converting the committed data from the first formatinto a common format; converting the data from the one or more sourcetables into the common format; joining the committed data in the commonformat and the data from the one or more source tables in the commonformat to generate joined data; and executing a query based on thejoined data.

Example 6. The method of any of examples 1-5, further comprising:retrieving expression properties of the received data; and pruning thereceived data based on the expression properties and the query.

Example 7. The method of any of examples 1-6, wherein the received dataincludes sequencing information.

Example 8. The method of any of examples 1-7, wherein ordering of thereceived data is maintained based on the sequencing information.

Example 9. A system comprising: at least one hardware processor; and atleast one memory storing instructions that, when executed by the atleast one hardware processor, cause the at least one hardware processorto perform operations implementing any one of example methods 1 to 8.

Example 10. A machine-readable storage device embodying instructionsthat, when executed by a machine, cause the machine to performoperations implementing any one of example methods 1 to 8.

What is claimed is:
 1. A method comprising: maintaining a source tablein a data system; storing new data from a client device for ingestioninto the source table in a storage in a first format; committing the newdata in the storage to make the new data accessible for query processingbefore the new data is ingested into the source table; receiving aquery; determining that the query relates to data stored in a sourcetable and committed data for the source table but not ingested in thesource table; converting the committed data from the first format into acommon format; converting the data from the source table from a secondformat into the common format; joining the committed data in the commonformat and the data from the source table in the common format togenerate joined data; and executing a query based on the joined data. 2.The method of claim 1, further comprising: retrieving expressionproperties of the committed data; and pruning the committed data basedon the expression properties and the query.
 3. The method of claim 2,wherein the expression properties include statistics of the committeddata.
 4. The method of claim 1, further comprising: generating a hybridtable based on data stored in the source table and the committed data.5. The method of claim 1, wherein the committed data is organized intoper-table chunks.
 6. The method of claim 5, wherein the per-table chunksare organized by per-account blobs.
 7. The method of claim 1, whereinthe committed data is stored in a different location than the sourcetable.
 8. A machine-storage medium embodying instructions that, whenexecuted by a machine, cause the machine to perform operationscomprising: maintaining a source table in a data system; storing newdata from a client device for ingestion into the source table in astorage in a first format; committing the new data in the storage tomake the new data accessible for query processing before the new data isingested into the source table; receiving a query; determining that thequery relates to data stored in a source table and committed data forthe source table but not ingested in the source table; converting thecommitted data from a first format into a common format; converting thedata from the source table from a second format into the common format;joining the committed data in the common format and the data from thesource table in the common format to generate joined data; and executinga query based on the joined data.
 9. The machine-storage medium of claim8, further comprising: retrieving expression properties of the committeddata; and pruning the committed data based on the expression propertiesand the query.
 10. The machine-storage medium of claim 9, wherein theexpression properties include statistics of the committed data.
 11. Themachine-storage medium of claim 8, further comprising: generating ahybrid table based on data stored in the source table and the committeddata.
 12. The machine-storage medium of claim 8, wherein the committeddata is organized into per-table chunks.
 13. The machine-storage mediumof claim 12, wherein the per-table chunks are organized by per-accountblobs.
 14. The machine-storage medium of claim 8, wherein the committeddata is stored in a different location than the source table.
 15. Asystem comprising: at least one hardware processor; and at least onememory storing instructions that cause the at least one hardwareprocessor to perform operations comprising: maintaining a source tablein a data system; storing new data from a client device for ingestioninto the source table in a storage in a first format; committing the newdata in the storage to make the new data accessible for query processingbefore the new data is ingested into the source table; receiving aquery; determining that the query relates to data stored in a sourcetable and committed data for the source table but not ingested in thesource table; converting the committed data from a first format into acommon format; converting the data from the source table from a secondformat into the common format; joining the committed data in the commonformat and the data from the source table in the common format togenerate joined data; and executing a query based on the joined data.16. The system of claim 15, the operations further comprising:retrieving expression properties of the committed data; and pruning thecommitted data based on the expression properties and the query.
 17. Thesystem of claim 16, wherein the expression properties include statisticsof the committed data.
 18. The system of claim 15, the operationsfurther comprising: generating a hybrid table based on data stored inthe source table and the committed data.
 19. The system of claim 15,wherein the committed data is organized into per-table chunks.
 20. Thesystem of claim 19, wherein the per-table chunks are organized byper-account blobs.